Sensor systems incorporating a plurality of sensors (multi-sensor systems) are widely used for a variety of military applications including ocean surveillance, air-to-air and surface-to-air defense (e.g., self-guided munitions), battlefield intelligence, surveillance and target detection (classification), and strategic warning and defense. Also, multi-sensor systems are used for a plurality of civilian applications including condition-based maintenance, robotics, automotive safety, remote sensing, weather forecasting, medical diagnoses, and environmental monitoring (e.g., weather forecasting).
To obtain the full advantage of a multi-sensor system, an efficient data fusion method (or architecture) may be selected to optimally combine the received data from the multiple sensors to generate a decision output. For military applications (especially target recognition), a sensor-level fusion process is widely used wherein data received by each individual sensor is fully processed at each sensor before being output to a system data fusion processor that generates a decision output (e.g., “validated target” or “no desired target encountered”) using at least one predetermined multi-sensor algorithm. The data (signal) processing performed at each sensor may include a plurality of processing techniques to obtain desired system outputs (target reporting data) such as feature extraction, and target classification, identification, and tracking. The processing techniques may include time-domain, frequency-domain, multi-image pixel image processing techniques, and/or other techniques to obtain the desired target reporting data.
Currently, a data fusion method (strategy) that is widely used for multi-sensor systems is multiplicative fusion that uses a predetermined algorithm incorporating a believe function theory (e.g., Dempster's Combination Rule or Dempster-Shafer Evidence Theory, Bayes, etc.) to generate reliability (likelihood or probability) function(s) for the system. During data fusion operation, belief function theories are used to model degrees of belief for making (critical) decisions based on an incomplete information set (e.g., due to noise, out of sensor range, etc.). The belief functions are used to process or fuse the limited quantitative data (clues) and information measurements that form the incomplete information set.
However, many current multi-sensor systems use fusion algorithms which assume a high signal-to-ratio (SNR) for each sensor (ignoring the noise energy level) and therefore generate reliability functions only associated with the desired object (e.g., target, decoy) leading to probability and decision output errors. One well-known belief function theory is the traditional Dempster-Shafer (D-S) theory which is presented in Appendix A. D-S theory may start with a finite (exhaustive), mutually exclusive set of possible answers to a question (e.g., target, decoy, noise for a target detection system) which is defined as the frame of discernment (frame defined by the question). D-S theory may then use basic probability assignments (BPAs) based on the generated elements within an information set (set of all propositions discerned by the frame of discernment) to make decisions. In situations where the frame of discernment includes at three terms, the information set may include singleton (only one element), partial ignorance (at least two elements), and total ignorance (all elements) terms. As shown in Table 1 and Table 2 (in Appendices A,B) for traditional D-S theory, all sensors in the system may assume high SNR to produce a plurality (e.g., four—{t}, {d}, {t,d}, {φ}) of BPM mass terms not associated with noise which may lead to (system) decision output errors.
Also, high SNR fusion methods are commonly multiplicative fusion methods which multiply a plurality of probability functions (generated from the received data from each individual sensor) to produce a single term (value). The generation of the single term makes it complex to weight contributions from the plurality of sensors (which may have different reliability values over different tracking time periods due to different sensor constraints, atmospheric conditions, or other factors) and thus may produce a less accurate data fusion output (decision output regarding target classification). Additionally, when the likelihood function readings of the sensors are close to zero, multiplicative fusion may provide a less reliable output.
Therefore, due to the disadvantages of the current multiplicative data fusion methods including belief function theories used for a multi-sensor system, there is a need to provide a multi-sensor system that uses an additive data fusion method including a modified belief function theory for better adaptive weighting and to produce multiple reliability terms including reliability terms associated with noise for low SNR situations.